To explain how demand forecasting is determined for Amazon, let's first break down the typical process, particularly in the context of Ren et al. (2020) and using historical sales data. Demand Forecasting Process Historical Sales Data: Amazon relies on its vast data of previous sales (old demands). By analyzing trends and patterns in this historical data, forecasters can predict future demand. Historical sales serve as a foundation for building forecasting models. The data for each product can include seasonal trends, spikes during holidays, or the effects of promotions. Quantitative Variables: According to Ren et al. (2020), to forecast demand, it is essential to focus on two quantitative variables. These variables could be: Sales volume: The number of units sold during specific periods. Price: The price of the product, which can affect the demand. Graphical Analysis: A graphical representation of past sales (like the one mentioned in your query) shows how demand fluctuates over time. The graph could visualize trends such as: Upward or downward sales trends. Seasonal variations (for example, increased sales in December). Anomalies or outliers (sales spikes or drops due to external factors like promotions or disruptions). Quantitative Methods: Time Series Analysis: This method is used to predict